def extract_hrv(rri):
features = []
hrv_data = hrv.time_domain(rri)
hrv_data.update(hrv.frequency_domain(rri))
keys = list(hrv_data.keys())
keys.sort()
for key in keys:
features.append(hrv_data[key])
return features
def r_features(r_peaks):
times = np.diff(r_peaks)
avg = np.mean(times)
filtered = sum([1 if i < 0.5 * avg else 0 for i in times])
total = len(r_peaks)
data = hrv.time_domain(times)
data['filtered_r'] = filtered
data['rel_filtered_r'] = filtered / total
# RRI Velocity
diff_rri = np.diff(times)
data.update(add_suffix(hrv.time_domain(diff_rri), "fil1"))
# RRI Acceleration
diff2_rri = np.diff(diff_rri)
data.update(add_suffix(hrv.time_domain(diff2_rri), "fil2"))
return data
def r_features(s, r_peaks):
r_vals = [s[i] for i in r_peaks]
times = np.diff(r_peaks)
avg = np.mean(times)
filtered = sum([1 if i < 0.5 * avg else 0 for i in times])
total = len(r_vals) if len(r_vals) > 0 else 1
data = hrv.time_domain(times)
data['beats_to_length'] = len(r_peaks) / len(s)
data['r_mean'] = np.mean(r_vals)
data['r_std'] = np.std(r_vals)
data['filtered_r'] = filtered
data['rel_filtered_r'] = filtered / total
return data
def r_features(r_peaks):
# Sanity check after artifact removal
if len(r_peaks) < 5:
print(
"NeuroKit Warning: ecg_hrv(): Not enough normal R peaks to compute HRV :/"
)
r_peaks = [1000, 2000, 3000, 4000]
hrv_dict = dict()
RRis = np.diff(r_peaks)
RRis = RRis / 500
RRis = RRis.astype(float)
# Artifact detection - Statistical
rr1 = 0
rr2 = 0
rr3 = 0
rr4 = 0
median_rr = np.median(RRis)
for index, rr in enumerate(RRis):
# Remove RR intervals that differ more than 25% from the previous one
if rr < 0.6:
rr1 += 1
if rr > 1.3:
rr2 += 1
if rr < median_rr * 0.75:
rr3 += 1
if rr > median_rr * 1.25:
rr4 += 1
# Artifacts treatment
hrv_dict["n_Artifacts1"] = rr1 / len(RRis)
hrv_dict["n_Artifacts2"] = rr2 / len(RRis)
hrv_dict["n_Artifacts3"] = rr3 / len(RRis)
hrv_dict["n_Artifacts4"] = rr4 / len(RRis)
hrv_dict["RMSSD"] = np.sqrt(np.mean(np.diff(RRis)**2))
hrv_dict["meanNN"] = np.mean(RRis)
hrv_dict["sdNN"] = np.std(RRis, ddof=1) # make it calculate N-1
hrv_dict["cvNN"] = hrv_dict["sdNN"] / hrv_dict["meanNN"]
hrv_dict["CVSD"] = hrv_dict["RMSSD"] / hrv_dict["meanNN"]
hrv_dict["medianNN"] = np.median(abs(RRis))
hrv_dict["madNN"] = mad(RRis, constant=1)
hrv_dict["mcvNN"] = hrv_dict["madNN"] / hrv_dict["medianNN"]
nn50 = sum(abs(np.diff(RRis)) > 50)
nn20 = sum(abs(np.diff(RRis)) > 20)
hrv_dict["pNN50"] = nn50 / len(RRis) * 100
hrv_dict["pNN20"] = nn20 / len(RRis) * 100
hrv_dict["Shannon"] = complexity_entropy_shannon(RRis)
hrv_dict["Sample_Entropy"] = nolds.sampen(RRis, emb_dim=2)
#mse = complexity_entropy_multiscale(RRis, max_scale_factor=20, m=2)
#hrv_dict["Entropy_Multiscale_AUC"] = mse["MSE_AUC"]
hrv_dict["Entropy_SVD"] = complexity_entropy_svd(RRis, emb_dim=2)
hrv_dict["Entropy_Spectral_VLF"] = complexity_entropy_spectral(
RRis, 500, bands=np.arange(0.0033, 0.04, 0.001))
hrv_dict["Entropy_Spectral_LF"] = complexity_entropy_spectral(
RRis, 500, bands=np.arange(0.04, 0.15, 0.001))
hrv_dict["Entropy_Spectral_HF"] = complexity_entropy_spectral(
RRis, 500, bands=np.arange(0.15, 0.40, 0.001))
hrv_dict["Fisher_Info"] = complexity_fisher_info(RRis, tau=1, emb_dim=2)
#hrv_dict["FD_Petrosian"] = complexity_fd_petrosian(RRis)
#hrv_dict["FD_Higushi"] = complexity_fd_higushi(RRis, k_max=16)
hrv_dict.update(hrv.time_domain(RRis))
hrv_dict.update(hrv.frequency_domain(RRis))
# RRI Velocity
diff_rri = np.diff(RRis)
hrv_dict.update(add_suffix(hrv.time_domain(diff_rri), "fil1"))
hrv_dict.update(add_suffix(hrv.frequency_domain(diff_rri), "fil1"))
# RRI Acceleration
diff2_rri = np.diff(diff_rri)
hrv_dict.update(add_suffix(hrv.time_domain(diff2_rri), "fil2"))
hrv_dict.update(add_suffix(hrv.frequency_domain(diff2_rri), "fil2"))
return hrv_dict